Speaker dependent ASRs for huastec and western-huastec náhuatl languages

Abstract

The purpose of this work is to show the results obtained when the latest technological advances in the area of Automatic Speech Recognition (ASR) are applied to the Western-Huastec Náhuatl and Huastec languages. Western-Huastec Náhuatl and Huastec are not only native (indigenous) languages in México, but also minority languages, and people who speak these languages usually are analphabetic. A speech database was created by recording the voice of native speaker when reading a set of documents used for native bilingual primary school in the official mexican state education system. A pronunciation dictionary was created for each language. A continuous Hidden Markov Models (HMM) were used for acoustical modeling, and bigrams were used for language Modeling. A Viterbi decoder was used for recognition. The word error rate of this task is below 8.621% for Western-Huastec Náhuatl language and 10.154% for Huastec language. © Springer-Verlag Berlin Heidelberg 2005.